Machine Learning in Credit Underwriting

A more efficient and broader access to SMBs Lending not Fascism (Authoritarian Nationalism) will result in job creation and economic growth.

Economic growth and job creation comes from SMBs, not Politicians.The core to American economic competitiveness or any dynamic free-market economies around the world is small to mid-size businesses — SMBs.

SMBs are critical in job creation in U.S economy. SMBs employ half of all jobs in the private sector — around 120 Million people but create 2/3 of all net new jobs in America. There are 28.7 Million SMBs in US with 80% being sole proprietorships (around 23 Mil) and 20% are 5.7 small firms with employees like mom-pop businesses, small-to-mid size suppliers, larger corporation and high growth startup.

Bank loans are critical to SMBs since they lack access to public institutional debt and equity capital market. However, it is getting harder for SMBs to have access to loans. SMBs lending continues to fall, while large business lending rises.The banking industry in the aggregate appears increasingly less focused on SMBs lending.A decades‐long trend toward consolidation of banking assets in fewer institutions is eliminating a key source of capital for small firms.

Search costs in SMBs lending are high, for both borrowers and lendersSMBs loan, often defined as business loans below $1 million, are considerably less profitable than large business loans. Assessing creditworthiness of SMBs can be difficult due to information asymmetry.

Costs of underwriting SMBs lending are also high. The diversity of small business loans’ purpose has made it difficult to securitize and sell pools of small business loans in the secondary market.Transaction costs to process a $100,000 loan are comparable to a $1 million loan, but with less profit .

Banks normally do not lend less than $100,000 or to businesses with revenue that is less than $2,000,000 since its less profitable to the bank.However, more than half of SMBs seek loan less than $100,000.

New online marketplaces are disrupting the traditional market for SMBs loans.ONDeck and Kabbage are raising capital from institutional investors including hedge fund and using proprietary risk model ( including non-traditional data) to lend to SMBs.Lending Club, Prosper and Funding Circle are connecting capital from institutional and retail investors with prime and subprime quality borrowers.

Current method of SMBs Credit UnderwritingIn the 1950s, Bill Fair and Earl Isaac at the Stanford Research Institute — SRI used Linear Regression to build a model for score card and rule set in credit score assessment.Later they found FICO (Fair Isaac Corporation), originally Fair, Isaac and Company in San Jose, California. The company focused on credit scoring services using this model, initially developed at SRI. Since then FICO score has become a fixture of consumer credit in United States.

FICO score is calculated based on 5 main factors:

35% Payment History ( Credit Repayment Habit)

30% Revolving Utilization ( Debt to Credit Limit Ratio)

15% Credit History Length ( Length of Credit History )

10% Types of Credit Used ( Diversity in type of Credit Used)

10% Inquiry Count.

FICO Score Distribution of US Population

Credit underwriting for SMBs is based mainly on FICO score. With 50% of population have 700 or better score, 25% is between 600–700 and 25% less than 600.

This means 50% of SMBs have access to cheap credit subsidized by the Feds, 25% SMBs pay premium to access to credit and 25% SMBs have no access to credit.Using machine learning to expand lending to larger population

Machine learning makes it possible to have a non-linear regression model to make credit risk assessment. Instead of traditional FICO score, we can use additional source of data like bill payment, social network, geolocation data, etcs to add more attributes to this credit assessment model.

We are seeing companies like Ondeck, Kabbage, Square , Paypal, Underwrite.ai taking this approach to create their Proprietary Risk Model for their SMBs lending.Underwrite.AI uses learning from medical research that uses machine learning to analyze 25,000 gene expressions and predict prostate cancerto their credit risk model.

They aggregated a 2500 attributes input from online applications, credit bureau files to their model and then apply Reinforcement Learning to optimize for a desired binary outcome among:Charge off or Full Payment.Profitable or Unprofitable.Positive Lifetime Value or Negative Lifetime Value.

Underwrite.AI also continuously retrains their model based on repayment and was able to help their 1st customer in SMBs to reduce default rate from 32.8% to 9% within 6 months.

Continuous advance in machine learning potentially solve more problems in information asymmetry in SMBs lending, expand credit access to larger population while reduce default rate and lower cost of financing.